Mercurial > repos > iuc > seurat
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"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/seurat commit 9812fde33003c6318f225fd7077a3a15b3a684a3"
author | iuc |
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date | Sat, 18 Jan 2020 12:57:27 -0500 |
parents | 321bdd834266 |
children | 82fcdf530f87 |
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<tool id="seurat" name="Seurat" version="@TOOL_VERSION@"> <description>- toolkit for exploration of single-cell RNA-seq data</description> <macros> <token name="@TOOL_VERSION@">3.1.2</token> </macros> <requirements> <requirement type="package" version="@TOOL_VERSION@">r-seurat</requirement> <requirement type="package" version="2.0">r-rmarkdown</requirement> <!-- Need to pin pandoc due to https://github.com/rstudio/rmarkdown/issues/1740 --> <requirement type="package" version="2.7.3">pandoc</requirement> </requirements> <command detect_errors="exit_code"><![CDATA[ #if "vln" in $meta.plots: #set $vln = 'T' #else #set $vln = 'F' #end if #if "feat" in $meta.plots: #set $feat = 'T' #else #set $feat = 'F' #end if #if "PCs" in $meta.plots: #set $PCs = 'T' #else #set $PCs = 'F' #end if #if "tsne" in $meta.plots: #set $tsne = 'T' #else #set $tsne = 'F' #end if #if "heat" in $meta.plots: #set $heatmaps = 'T' #else #set $heatmaps = 'F' #end if Rscript -e "library(\"rmarkdown\"); render(\"$__tool_directory__/Seurat.R\", params = list(counts = \"${counts}\", min_cells = \"${adv.min_cells}\", min_genes = \"${adv.min_genes}\", low_thresholds = \"${adv.low_thresholds}\", high_thresholds = \"${adv.high_thresholds}\", numPCs = \"${adv.num_PCs}\", cells_use = \"${adv.cells_use}\", resolution = \"${adv.resolution}\", min_pct = \"${adv.min_pct}\", logfc_threshold = \"${adv.logfc_threshold}\", warn = \"${meta.warn}\", varstate = \"${meta.varstate}\", showcode = \"${meta.showcode}\", vlnfeat = \"$vln\", featplot = \"$feat\", PCplots = \"$PCs\", tsne = \"$tsne\", heatmaps = \"$heatmaps\"), intermediates_dir = \".\", output_format = html_document(), output_dir = \".\", output_file = \"out.html\")" ]]></command> <inputs> <param name="counts" type="data" format="tabular,tsv" label="Counts file" help="The should be a TAB-separated count matrix with gene identifers in the first column and a header row"/> <section name="adv" title="Advanced Options" expanded="true"> <param name="num_PCs" type="integer" min="0" value="10" label="Number of PCs to use in plots" help="Uses this number of PCs in PCHEatmap, JackStrawPlot, FindClusters, RunTSNE. Default: 10" /> <param name="min_cells" type="integer" min="0" value="0" label="Minimum cells" help="Include genes with detected expression in at least this many cells." /> <param name="min_genes" type="integer" min="0" value="0" label="Minimum genes" help="Include cells where at least this many genes are detected." /> <param name="low_thresholds" type="integer" value="1" label="Low threshold for filtering cells" /> <param name="high_thresholds" type="integer" value="20000000" label="High threshold for filtering cells" /> <param name="cells_use" type="integer" min="1" value="500" label="Cells to use for PCHeatmap" help="Plots this number of top ‘extreme’ cells on both ends of the spectrum, which dramatically speeds plotting for large datasets" /> <param name="resolution" type="float" value="0.6" label="Resolution parameter" help="Value of the resolution parameter used in FindClusters, use a value above (below) 1.0 if you want to obtain a larger (smaller) number of communities." /> <param name="min_pct" type="float" value="0.1" label="Minimum percent cells" help="With FindMarkers only test genes that are detected in a minimum fraction of min.pct cells in either of the two populations. Meant to speed up the function by not testing genes that are very infrequently expressed. Default is 0.1" /> <param name="logfc_threshold" type="float" min="0" value="0.25" label="LogFC threshold" help="With FindMarkers, limit testing to genes which show, on average, at least X-fold difference (log-scale)between the two groups of cells. Default is 0.25 Increasing logfc.threshold speeds up the function, but can miss weaker signals." /> </section> <section name="meta" title="Output options" expanded="true"> <param name="showcode" type="boolean" truevalue="T" falsevalue="F" checked="false" label="Show code alongside outputs?"/> <param name="warn" type="boolean" truevalue="T" falsevalue="F" checked="false" label="Include warnings in the output file (Yes) or pipe to stdout (No)"/> <param name="varstate" type="boolean" truevalue="T" falsevalue="F" checked="false" label="Display variable values used in code at the beginning of output file?"/> <param name="plots" type="select" optional="true" multiple="true" display="checkboxes" label="Which plots should be output?"> <option value="vln" selected="true">Violin and Scatter plots</option> <option value="feat" selected="true">Feature counts plots</option> <option value="PCs" selected="true">PC plots</option> <option value="tsne" selected="true">tSNE plots</option> <option value="heat" selected="true">Heatmap plots</option> </param> </section> </inputs> <outputs> <data name="out_html" format="html" from_work_dir="out.html" label="${tool.name} on ${on_string}" /> </outputs> <tests> <test> <param name="counts" ftype="tabular" value="counts.tab.gz"/> <section name="adv"> <param name="numPCs" value="10" /> <param name="min_cells" value="3"/> <param name="min_genes" value="200"/> <param name="low_thresholds" value="1" /> <param name="high_thresholds" value="20000000" /> <param name="cells_use" value="500"/> <param name="resolution" value="0.6" /> <param name="min_pct" value="0.25" /> <param name="logfc_threshold" value="0.25" /> </section> <section name="meta"> <param name="showcode" value="T"/> <param name="warn" value="F"/> <param name="varstate" value="F"/> <param name="plots" value="feat"/> </section> <output name="out_html" ftype="html" value="out.html" compare="sim_size" delta="20000" /> </test> </tests> <help><![CDATA[ .. class:: infomark **What it does** Seurat_ is a toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. It is developed and maintained by the `Satija Lab`_ at NYGC. Seurat aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See the `Seurat Guided Clustering tutorial`_ for more information. ----- **Inputs** * Gene count matrix in TAB-separated format ----- **Outputs** * HTML of plots Optionally you can choose to output * Seurat RDS object (can use within R) * Rscript .. _Seurat: https://www.nature.com/articles/nbt.4096 .. _Satija Lab: https://satijalab.org/seurat/ .. _Seurat Guided Clustering tutorial: https://satijalab.org/seurat/pbmc3k_tutorial.html ]]></help> <citations> <citation type="doi">10.1038/nbt.4096</citation> </citations> </tool>